Medical ultrasound has gained increased popularity during the past two decades as a diagnostic tool. A number of techniques for tracking biological tissue using ultrasonic energy have been investigated, and some of these techniques are now considered standard clinical practice. These techniques may use the statistical temporal and spatial properties of the returned echo signal from the biological tissue. The returned signal can be analyzed either in the frequency or the time domain to determine motion information.
Frequency domain techniques, such as Doppler ultrasound, have been used clinically for some time. Frequency domain techniques may, however, suffer from fundamental performance limitations in certain clinical situations. The inability to detect non-axial movement, aliasing, and the inherent trade-off between spatial resolution and velocity amplitude resolution are a few examples of performance limitations associated with frequency domain techniques. For example, Doppler ultrasound estimates the velocity of biological tissue by measuring the shift in frequency between two ultrasonic scans reflected from the biological tissue. Doppler may not estimate the biological tissue velocity accurately, however, when the movement has a component in a direction other than along the ultrasonic energy's (axial) path of travel. Such a component of movement may not be detected by the Doppler technique because the movement (or a portion thereof) does not produce a frequency shift in the second ultrasound scan with respect to the first ultrasound scan. For example, if blood flow is in a direction that is normal to the path of ultrasonic energy, Doppler may not detect the movement.
Time domain techniques, such as correlation searching, may be based on tracking windowed speckle patterns from one ultrasonic acquisition to a later ultrasound acquisition. For example, a blind correlation search may involve obtaining two successive ultrasound scans of blood flow and analyzing the second scan for correlation with the first scan. The computing power used in a blind correlation search, however, may be significant, thereby possibly limiting the use of blind correlation search to off-line processing or to systems that have expensive dedicated processors, especially when applied to three-dimensional data. Correlation searching may, therefore, not be suitable for a real-time implementation. Moreover, a large kernel size may be needed to provide a more accurate correlation, however, the large kernel size may also reduce the spatial resolution of the velocity estimation.
Feature tracking may involve comparing the peak intensity values in a first ultrasound scan of tissue to the peak intensity values in a subsequent ultrasound scan corresponding to the same tissue. For example, U.S. Pat. No. 5,109,857 to Roundhill and von Ramm discloses peak tracking wherein a B-mode ultrasound scanner is used to obtain first and second ultrasound scans. The displacement of a particular peak in the first ultrasound scan is determined to be equal to the distance to the closest adjacent peak in the second ultrasound scan. This presumes a match between the two peaks, which may impose a limit on the magnitude of motion that can be tracked between successive scans.
Speckle noise and signal to noise ratio may affect the performance in tracking discrete features, such as peaks. In particular, speckle noise or a low signal to noise ratio may cause the peak intensity values to change over time. In view of the above discussion, there is a continued need for improved methods and systems for estimating blood flow velocity and tissue motion.